Last year we saw an explosion of data, but unfortunately, not aren’t enough people with the expertise to handle the increasing levels of data and computing. DAN SOMMER, Senior Director of Qlik, predicts that in 2017 a culture-wide change is needed.
Over the past twelve months we’ve seen an explosion of data, an increase in processing it and a move towards information activism. This means the number of employees actively able to work with – and master – the huge amounts of information available, such as data scientists, application developers, and business analysts, have become a valuable entity.
Unfortunately, however, there still aren’t enough people with the expertise to handle the ever-increasing, vast levels of data and computing. You would assume, with all the information currently being produced and held by businesses, that 2017 would see us in a new digital era of facts. But, without the right number of specialists to consume and analyse it, there’s a gap in resources. Data is, unfortunately, growing faster than our ability to make use of it.
For many business leaders then, this means a reliance on gut instinct to make even the most important decisions. Unable to hone in on the most important insights, they’re presented with multiple – and sometimes conflicting – data points, so the most important ones seem unreliable.
The situation needs to change. Yes, that will mean upskilling more data scientists in 2017, but there will be a greater focus on empowering more people more broadly. That will go beyond information activists and towards providing more people with the tools and training to increase data literacy. Just as reading and writing skills needed to move beyond scholars 100 years ago, data literacy will become one of the most important business skills for any member of staff.
So, what will change to see culture-wide data literacy become a reality? Here are my predictions:
1. Combinations of data – Big data will become less about size and more about combinations. With more fragmentation of data and most of it created externally in the cloud, there will be a cost impact to hoarding data without a clear purpose. That means we’ll move towards a model where businesses have to quickly combine their big data with small data so they can gain insights and context to get value from it as quickly as possible. Combining data will also shine a light on false information more easily, improving data accuracy as well as understanding.
2. Hybrid thinking – In 2017, hybrid cloud and multi-platform will emerge as the primary model for data analytics. Because of where data is generated, ease of getting started, and its ability to scale, we’re now seeing an accelerated move to cloud. But one cloud is not enough, because the data and workloads won’t be in one platform. In addition, data gravity also means that on premise has long staying power. Hybrid and multi-environment will emerge as the dominant model, meaning workloads and publishing will happen across cloud and on-premise.
3. Self-service for all – Freemium is the new normal, so 2017 will be the year users have easier access to their analytics. More and more data visualisation tools are available at low cost, or even for free, so some form of analytics will become accessible across the workforce. With more people beginning their analytics journey, data literacy rates will naturally increase — more people will know what they’re looking at and what it means for their organisation. That means information activism will rise too.
4. Scale-up – Much a result of its own success, user-driven data discovery from two years ago has become today’s enterprise-wide BI. In 2017, this will evolve to replace archaic reporting-first platforms. As modern BI becomes the new reference architecture, it will open more self-service data analysis to more people. It also puts different requirements on the back end for scale, performance, governance, and security.
5. Advancing analytics – In 2017, the focus will shift from “advanced analytics” to “advancing analytics.” Advanced analytics is critical, but the creation of the models, as well as the governance and curation of them, is dependent on highly-skilled experts. However, many more should be able to benefit from those models once they are created, meaning that they can be brought into self-service tools. In addition, analytics can be advanced by increased intelligence being embedded into software, removing complexity and chaperoning insights. But the analytical journey shouldn’t be a black box or too prescriptive. There is a lot of hype around “artificial intelligence,” but it will often serve best as an augmentation rather than replacement of human analysis because it’s equally important to keep asking the right questions as it is to provide the answers.
6. Visualisation as a concept will move from analysis-only to the whole information supply chain – Visualisation will become a strong component in unified hubs that take a visual approach to information asset management, as well as visual self-service data preparation, underpinning the actual visual analysis. Furthermore, progress will be made in having visualisation as a means to communicate our findings. The net effect of this is increased numbers of users doing more in the data supply chain.
7. Focus will shift to custom analytic apps and analytics in the app – Everyone won’t — and cannot be —both a producer and a consumer of apps. But they should be able to explore their own data. Data literacy will therefore benefit from analytics meeting people where they are, with applications developed to support them in their own context and situation, as well as the analytics tools we use when setting out to do some data analysis. As such, open, extensible tools that can be easily customised and contextualised by application and web developers will make further headway.
These trends lay the foundation for increased levels of not just information activism, but also data literacy. After all, new platforms and technologies that can catch “the other half” (i.e., less skilled information workers and operational workers on the go) will help usher us into an era where the right data becomes connected with people and their ideas — that’s going to close the chasm between the levels of data we have available and our ability to garner insights from it. Which, let’s face it, is what we need to put us on the path toward a more enlightened, information-driven, and fact-based era.
Why your first self-driving car ride will be in a robotaxi
Autonomous driving will take longer than we expect, and involve less ownership than the industry would like, writes Intel’s AMNON SHASHUA
As we all watch automakers and autonomous tech companies team up in various alliances, it’s natural to wonder about their significance and what the future will bring. Are we realizing that autonomous driving technology and its acceptance by society could take longer than expected? Is the cost of investing in such technology proving more than any single organization can sustain? Are these alliances driven by a need for regulation that will be accepted by governments and the public or for developing standards on which manufacturers can agree?
The answers are likely a bit of each, which makes it a timely opportunity to review the big picture and share our view of where Intel and Mobileye stand in this landscape.
Three Aspects to Auto-Tech-AI
There are three aspects to automotive-technology-artificial intelligence (auto-tech-AI) that are unfolding:
- Advanced driver-assistance systems (ADAS)
- Robotaxi ride-hailing as the future of mobility-as-a-service (MaaS)
- Series-production passenger car autonomy
With ADAS technologies, the driver remains in control while the system intervenes when necessary to prevent accidents. This is especially important as distracted driving grows unabated. Known as Levels 0-2 as defined by the Society of Automotive Engineers (SAE), ADAS promises to reduce the probability of an accident to infinitesimal levels. This critical phase of auto-tech-AI is well underway, with today’s penetration around 22%, a number expected to climb sharply to 75% by 2025.1
Meanwhile, the autonomous driving aspect of auto-tech-AI is coming in two phases: robotaxi MaaS and series-production passenger car autonomy. What has changed in the mindset of many companies, including much of the auto industry, is the realization that those two phases cannot proceed in parallel.
Series-production passenger car autonomy (SAE Levels 4-5) must wait until the robotaxi industry deploys and matures. This is due to three factors: cost, regulation and geographic scale. Getting all factors optimized simultaneously has proven too difficult to achieve in a single leap, and it is why many in the industry are contemplating the best path to achieve volume production. Many industry leaders are realizing it is possible to stagger the challenges if the deployment of fully autonomous vehicles (AVs) aims first at the robotaxi opportunity.
Cost: The cost of a self-driving system (SDS) with its cameras, radars, lidars and high-performance computing is in the tens of thousands of dollars and will remain so for the foreseeable future. This cost level is acceptable for a driverless ride-hailing service, but is simply too expensive for series-production passenger cars. The cost of SDS should be no more than a few thousand dollars – an order of magnitude lower than today’s costs – before such capability can find its way to series-production passenger cars.
Regulation: Regulation is an area that receives too little attention. Companies deep in the making of SDSs know that it is the stickiest issue. Beside the fact that laws for granting a license to drive are geared toward human drivers, there is the serious issue of how to balance safety and usefulness in a manner that is acceptable to society.
It will be easier to develop laws and regulations governing a fleet of robotaxis than for privately-owned vehicles. A fleet operator will receive a limited license per use case and per geographic region and will be subject to extensive reporting and back-office remote operation. In contrast, licensing such cars to private citizens will require a complete overhaul of the complex laws and regulations that currently govern vehicles and drivers.
The auto industry is gradually realising that autonomy must wait until regulation and technology reach equilibrium, and the best place to get this done is through the robotaxi phase.
Scale: The third factor, geographic scale, is mostly a challenge of creating high-definition maps with great detail and accuracy, and of keeping those maps continuously updated. The geographic scale is crucial for series-production driverless cars because they must necessarily operate “everywhere” to fulfil the promise of the self-driving revolution. Robotaxis can be confined to geofenced areas, which makes it possible to postpone the issue of scale until the maturity of the robotaxi industry.
When the factors of cost, regulation and scale are taken together, it is understandable why series-production passenger cars will not become possible until after the robotaxi phase.
As is increasingly apparent, the auto industry is gravitating towards greater emphasis on their Level 2 offerings. Enhanced ADAS – with drivers still in charge of the vehicle at all times – helps achieve many of the expected safety benefits of AVs without bumping into the regulatory, cost and scale challenges.
At the same time, automakers are solving for the regulatory, cost and scale challenges by embracing the emerging robotaxi MaaS industry. Once MaaS via robotaxi achieves traction and maturity, automakers will be ready for the next (and most transformative) phase of passenger car autonomy.
The Strategy for Autonomy
With all of this in mind, Intel and Mobileye are focused on the most efficient path to reach passenger car autonomy. It requires long-term planning, and for those who can sustain the large investments ahead, the rewards will be great. Our path forward relies on four focus areas:
- Continue at the forefront of ADAS development. Beyond the fact that ADAS is the core of life-saving technology, it allows us to validate the technological building blocks of autonomous vehicles via tens of new production programs a year with automakers that submit our technology to the most stringent safety testing. Our ADAS programs – more than 34 million vehicles on roads today – provide the financial “fuel” to sustain autonomous development activity for the long run.
- Design an SDS with a backbone of a camera-centric configuration. Building a robust system that can drive solely based on cameras allows us to pinpoint the critical safety segments for which we truly need redundancy from radars and lidars. This effort to avoid unnecessary over-engineering or “sensor overload” is key to keeping the cost low.
- Build on our Road Experience Management (REM)™ crowdsourced automatic high-definition map-making to address the scale issue. Through existing contracts with automakers, we at Mobileye expect to have more than 25 million cars sending road data by 2022.
- Tackle the regulatory issue through our Responsibility-Sensitive Safety (RSS) formal model of safe driving, which balances the usefulness and agility of the robotic driver with a safety model that complies with societal norms of careful driving.
At Intel and Mobileye, we are all-in on the global robotaxi opportunity. We are developing technology for the entire robotaxi experience – from hailing the ride on your phone, through powering the vehicle and monitoring the fleet. Our hands-on approach with as much of the process as possible enables us to maximize learnings from the robotaxi phase and be ready with the right solutions for automakers when the time is right for series-production passenger cars.
On the way, we will help our partners deliver on the life-saving safety revolution of ADAS. We are convinced this will be a powerful and historic example of the greatest value being realized on the journey.
Professor Amnon Shashua is senior vice president at Intel Corporation and president and chief executive officer of Mobileye, an Intel company.
Sea of Solitude represents mental health issues through gaming
It’s a game that provides a tasteful visual representation of mental health issues. BRYAN TURNER dives into the Sea of Solitude.
Disclaimer: This review is based on four hours of gameplay.
Sea of Solitude, the latest adventure game by Jo-Mei Games and EA Games, takes a sobering look at loneliness. It represents this loneliness visually, using light and dark environmental changes, as well as creatures players must encounter. The main character, Kay, must make it through the sea without finding herself trapped in a sea of loneliness. She meets fantastical creatures along her journey, and she must help them solve their challenges while keeping herself in a sane environment.
The game is systematic in the way it represents its important aspects. It starts with a striking visual art style and a soft storyline, which gives characters a chance to absorb the beauty of the game. As one gets a hang of the controls and used to the art style, the story kicks it up a few notches to reveal the harrowing backstories of the creatures that reside in the sea Kay must travel.
In particular, it features a creature that keeps flying away from Kay. This was frustrating because the previous chapter of the game presents a backstory for the creature that was not only devastating to the main character, but also to the player. Once Kay meets this creature, players must be ready to cry. It’s a brilliantly crafted story and hats off to Jo-Mei Games for being great storytellers.
Cornelia Geppert, CEO of Jo-Mei Games, told EA: “Sea of Solitude centres on the essence of loneliness and tugs on the heartstrings of its players by mirroring their own reality. It’s by far the most artistic and personal project I’ve ever created, written during a very emotional time in my life. Designing characters based on emotions was a deeply personal achievement for our team and we’re so excited for players to soon experience Kay’s powerful story of self-discovery and healing.”
Generally, I steer clear of games that are metaphors about mental health issues because they tend to be crass in how they address mental health. Sea of Solitude is quite different because of its level of relatability. Other games about mental health tend to be about a specific disorder that not many people experience, while loneliness is something that so many of us experience. Additionally, the representation of how loneliness affects Kay in the real world is sharp but tasteful. The combination of relatability and respectful representation is what makes the game’s story so brilliant.
Another great aspect of this game is the music scoring. It uses sound and the absence of sound very carefully to invoke the right feelings expected from players. The game wouldn’t be as good with the sound off and subtitles on, so future players are recommended to turn up the volume or put on headphones.
The game is long for an indie game, at around three or four hours of gameplay until the end is reached. Several sources say there is a hidden ending, so players can look out for that in a second playthrough.
The game’s story isn’t perfect, though. The eventual sameness of creature encounters is a little disappointing. This may be down to the expectation of being extremely devastated by all the stories of the creatures, especially when one is less than devastated by the subsequent stories. One of the most affecting creature stories was also presented at the beginning of the game, which set the bar very high for the rest of the creatures.
One creature, in particular, tries very hard to have the greatest emotional impact, but this comes across as blunt and dampens the meaning of what it was supposed to represent.
While I didn’t mind sharp representation, the perception of themes like bullying, estrangement, and suicidal thoughts may vary in appropriateness from player to player. Prospective players with existing painful mental health issues should consult gameplay videos, like the one below, before purchasing the game, to gauge appropriateness.
Overall, the game is incredible at connecting with what it is to be human and what it means to be lonely. Dealing with issues as physical creatures is a great touch, as the main character tends to resolve the problems of the creature by understanding what the problems mean.